{"data":{"event":{"id":"dcea3b2a-1bda-4d41-8fc9-8672d591dcd5","slug":"adversarial-random-forests-for-omics-synthesis-7195ed3c15","title":"\n\nAdversarial random forests for omics synthesis \n\n","short_summary":"Data availability is critical for understanding complex disease pathways and developing robust predictive models. Although high-throughput omics technologies have improved insight into disease mechanisms, data acquisition from inaccessible tissues such as the central nervous system remains a major limitation, causing small sample sizes and complicating early prediction of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Generative modeling has emerged as a powerful approach for synthesizing data to support downstream clustering and prediction with small sample size, bu","full_description":"Data availability is critical for understanding complex disease pathways and developing robust predictive models. Although high-throughput omics technologies have improved insight into disease mechanisms, data acquisition from inaccessible tissues such as the central nervous system remains a major limitation, causing small sample sizes and complicating early prediction of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Generative modeling has emerged as a powerful approach for synthesizing data to support downstream clustering and prediction with small sample size, but existing methods rarely handle high-dimensional tabular omics data effectively. Adversarial random forests (ARFs) provide a well-performing framework for tabular data generation but are not designed for high-dimensional settings. To address this limitation, we introduce high-dimensional ARF (h-ARF), an extension of ARF optimized for integrated clinical and high-dimensional omics data. Using benchmarks across nine datasets and eight performance metrics, we show that h-ARF better preserves both feature distributions, and downstream clustering and prediction utilities compared with ARFs. The method is implemented in the open-source R package harf, available on CRAN.\n\nUpdate (2026-07-16): Our approach to bioresilience. Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience and AI models.\n\nUpdate (2026-09-18): \n\nTime-Series Foundation Models for Cognitive Workload Classification using Eye-Tracking Data \n\n. Cognitive workload (CWL) assessment is relevant to a range of applications, such as monitoring driver fatigue and pilot attention, surgeon workload during complex procedures, and astronaut cognitive fatigue during long-duration missions. Eye-tracking datasets are generally small, which hinders the generalizability of the AI models. Time-series foundation models (TSFMs) have shown promise in mitigating this limitation as they are pretrained on large corpora and can be effectively fine-tuned with limited task-specific data. Although cognitive workload measurement has been explored in controlled settings, the ability of TSFMs to generalize to unseen individuals for CWL classification from eye-tracking data has not been studied. In this paper, two TSFMs, MOMENT and Moirai, were evaluated for CWL classification and compared against CNN, FFN, and LSTM baselines on two publicly available eye-tracking datasets. We used subject-level five-fold cross-validation in which data from each test subject were held out during training. 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Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-15T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-16T05:00:51.612Z","model_slug":"unspecified-ai-system","model_name":"Unspecified AI system","identity_class":"unknown","is_internal":false,"family_name":"Unspecified","family_slug":"unknown-unspecified","organization_name":"Unknown","organization_slug":"unknown"}],"sources":[{"id":"02551f59-0801-4cd0-bc51-7edb4acda061","event_id":"dcea3b2a-1bda-4d41-8fc9-8672d591dcd5","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.09.750490v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.09.750490v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-15T00:00:00.000Z","retrieved_at":"2026-09-16T05:00:51.619Z","title":"\n\nAdversarial random forests for omics synthesis \n\n","excerpt":"Data availability is critical for understanding complex disease pathways and developing robust predictive models. Although high-throughput omics technologies have improved insight into disease mechanisms, data acquisition from inaccessible tissues such as the central nervous system remains a major limitation, causing small sample sizes and complicating early prediction of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Generative modeling has emerged as a powerful appro","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-16T05:00:51.619Z"},{"id":"90317cca-f9bb-45d2-95db-c8b5a39639e3","event_id":"dcea3b2a-1bda-4d41-8fc9-8672d591dcd5","url":"https://deepmind.google/blog/our-approach-to-bioresilience/","canonical_url":"https://deepmind.google/blog/our-approach-to-bioresilience/","source_type":"developer_announcement","publisher":"Google DeepMind Blog","author":null,"publication_date":"2026-07-16T00:00:00.000Z","retrieved_at":"2026-09-17T03:01:00.787Z","title":"Our approach to bioresilience","excerpt":"Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience and AI models.","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-17T03:01:00.787Z"},{"id":"654c8b46-cab8-4f41-b741-51de96979851","event_id":"dcea3b2a-1bda-4d41-8fc9-8672d591dcd5","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.13.751282v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.13.751282v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-18T00:00:00.000Z","retrieved_at":"2026-09-19T07:00:55.571Z","title":"\n\nTime-Series Foundation Models for Cognitive Workload Classification using Eye-Tracking Data \n\n","excerpt":"Cognitive workload (CWL) assessment is relevant to a range of applications, such as monitoring driver fatigue and pilot attention, surgeon workload during complex procedures, and astronaut cognitive fatigue during long-duration missions. Eye-tracking datasets are generally small, which hinders the generalizability of the AI models. 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